9results about How to "Accurate explanation" patented technology

The invention provides a mammary gland ultrasonic focus interpretable diagnosis system, a storage medium and electronic equipment, and relates to the technical field of medical image artificial intelligence auxiliary diagnosis. According to the method, image attribute feature tags are mapped to corresponding nodes of a knowledge graph. Then, the graph neural network model carries out multi-hop reasoning on a graph structure through a message passing mechanism; according to the method, not only can information from related attribute nodes be aggregated, but also the reasoning result of the graph neural network model can be calibrated and explained by utilizing clinical expert rules extracted from the knowledge graph, so that a clear and traceable reasoning path is attached to each prediction of the graph neural network model. The technical problem that image information is separated from the knowledge reasoning process in an existing knowledge graph fusion ultrasonic focus diagnosis method is solved, the visual perception ability of the image and the symbol reasoning ability of the knowledge graph are combined, and therefore an intelligent diagnosis system which is accurate in prediction and clear in explanation is provided.

The application discloses a kind of causal common sense knowledge base construction methods based on graph attention mechanism, comprising the following steps: 1) obtain causal common sense knowledge triples from open knowledge base as training resources;2) using knowledge graph embedding technology, obtain the vector representation of causal common sense knowledge triples;3) combined with background knowledge graph, construct causal common sense knowledge base construction model based on graph attention mechanism, select appropriate loss function to optimize model parameters;4) using causal common sense knowledge base construction model to score the confidence of missing triples, the highest score triples are added to the knowledge base as new learning causal common sense knowledge.The application is based on background knowledge graph, and constructs causal common sense knowledge base construction model using graph attention mechanism, intends to learn new causal common sense knowledge through a small amount of training resources, expands causal common sense knowledge base, so that the knowledge base can better support intelligent question and answer system.

The invention belongs to the crossing field of artificial intelligence and geophysics, and particularly discloses a transient electromagnetic forward modeling method and system considering an induced polarization effect based on deep learning, and the method comprises the steps: inputting Cole-Cole key parameters representing the induced polarization effect into a CNN-LSTM mixed deep learning network model for forward modeling, and obtaining transient electromagnetic response data; wherein Cole-Cole key parameters representing the induced polarization effect serve as multi-dimensional input features of the CNN-LSTM mixed deep learning network model, transient electromagnetic response data of a corresponding time channel serve as output, and the CNN-LSTM mixed deep learning network model is trained by adopting a segmented weighted mean square error loss function. According to the method, the segmented weighted mean square error loss function is adopted in the training stage, higher weights are given to the symbol inversion points and the prediction errors of the adjacent time windows, and the reliability of all-time response prediction is guaranteed.

The invention provides a spectrum inversion high-resolution processing method based on strong axis stripping, and relates to the field of seismic exploration, and the technical scheme is that the spectrum inversion high-resolution processing method based on strong axis stripping is characterized by comprising the following steps: inputting original seismic data, and extracting strong reflection wave event seismic data; subtracting the extracted strong reflection wave event seismic data from the original seismic data to obtain seismic data after strong reflection suppression; replacing the original seismic data with the seismic data subjected to strong reflection suppression, and establishing a spectrum inversion objective function; and solving the spectral inversion objective function to obtain a high-resolution seismic section which suppresses strong reflected waves and highlights weak signals. The method has the advantages that a spectral inversion high-resolution processing mechanism based on strong axis stripping is clear, and time domain iteration deconvolution stripping of the strong reflection wave event, spectral inversion high-resolution processing stripping of the strong reflection wave event, strong reflection suppression and spectral inversion high-resolution thin layer recognition are involved.

The invention discloses a blood pressure measuring method, wearable equipment and a storage medium, and relates to the technical field of wearable equipment, and the blood pressure measuring method comprises the following steps: acquiring a target physiological signal detected by a target user in a current time period based on the wearable equipment; collecting detection environment parameters of the wearable device in the current time period; according to the target physiological signal, blood pressure change factor information which causes the change of the blood pressure value level of the target user is identified, and a target blood pressure calculation model mapped by the blood pressure change factor information is determined from a plurality of preset blood pressure calculation models; and inputting the acquired blood pressure baseline characteristics and blood pressure baseline states, the target physiological signals and the detection environment parameters into a target blood pressure calculation model, and outputting a blood pressure value of the target user. Interference caused by individual differences is effectively reduced, and accuracy and individual adaptability of blood pressure monitoring are improved.

This invention discloses a method for interpreting non-uniform fracture parameters based on fracture propagation simulation forward modeling and pump shutdown pressure drop analysis inversion. The method includes: (1) obtaining geological parameters and construction parameters of the target well, and obtaining pressure data based on the fracturing construction process; (2) using cepstral analysis to identify the opening status of multiple perforation clusters in a single fracturing section based on high-frequency pressure data, and determining the location of the effectively opened perforation clusters among all perforation clusters; (3) using low-frequency pressure data to obtain the total area of ​​the first fracture in a single fracturing section based on the inversion theoretical model; (4) establishing a fracture propagation simulation model, using the location of the effectively opened perforation clusters and the total area of ​​the first fracture as physical constraints, and simulating the total area of ​​the second fracture. When the difference between the total area of ​​the second fracture and the total area of ​​the first fracture is less than a set threshold, the interpretation of non-uniform fracture parameters in a single fracturing section is achieved. This invention can achieve rapid and accurate interpretation of non-uniform fracture parameters.

This invention discloses a method for intelligent greeting, explanation, and lead generation in architectural exhibition halls using a digital human based on a Finite State Machine (FSM), belonging to the field of 3D virtual interaction and intelligent customer acquisition technology. The invention utilizes a real-time user behavior acquisition unit to collect 3D roaming behavior in milliseconds. The FSM control unit incorporates five mutually exclusive states and a standardized transition matrix, driving the digital human to intelligently switch between idle, greeting, precise explanation, lead generation guidance, and order placement states. The digital human driving unit employs lightweight rendering and lip-synced broadcasting for large architectural scenes. A scene-product linkage unit achieves native linkage between explanation and transaction. An intelligent guidance unit gently acquires high-intent leads. A data iteration unit optimizes interactive effects in a closed loop. This invention solves the industry pain points of traditional 3D exhibition halls, such as unattended operation, traffic loss, stiff interaction, disconnect between explanation and transaction, and inability to automatically acquire customers and generate leads. It achieves 365-day unattended intelligent operation, significantly improving user dwell time and business conversion rates. It is applicable to all scenarios, including 3D cloud exhibitions in the architectural industry chain, digital exhibition halls for building materials companies, industrial park investment promotion, and government and enterprise displays for urban renewal.

The invention provides an artificial intelligence interpretation method for multi-arm hole diameter logging data. The artificial intelligence interpretation method for the multi-arm hole diameter logging data comprises the steps that 1, imaging processing is conducted on logging interpretation data; 2, building a working condition interpretation model based on the convolutional neural network; step 3, training the built working condition interpretation model; 4, the well trained working condition interpretation model is utilized, and according to the collected actual multi-arm hole diameter logging data, the multi-arm hole diameter logging working condition is judged. According to the artificial intelligence interpretation method for the multi-arm hole diameter logging data, by combining the high-precision data acquisition capability of multi-arm hole diameter logging and the efficient intelligent analysis advantage of artificial intelligence, the interpretation efficiency and interpretation precision of multi-arm hole diameter logging interpretation work are effectively improved, and the labor intensity of workers is reduced.

This application provides a method and system for bandgap prediction and interpretation based on a multi-head self-attention mechanism. The method includes: using a Transformer-based encoder to model the global dependencies of a CSV file containing the physicochemical characteristics of lithium-ion battery materials through a multi-head self-attention mechanism, determining the context embedding, and concatenating it with the physicochemical characteristics of the lithium-ion battery materials to determine training features; then using a pre-set CatBoost regressor to perform regression training on the training features to determine the Catformer model; using the Catformer model to predict the bandgap of the lithium-ion battery material to be tested, and using the SHAP analysis method to perform interpretability analysis on the bandgap prediction. This application enhances the global dependency characteristics of the material, achieves accurate bandgap prediction, and reveals the physicochemical significance of the prediction results, providing a general tool for the rapid design of lithium battery electrode materials.